Existing radiomic and radiogenomic studies typically consider voxel-level summary features (based on intensity histogram, geometry, and texture analyses) as representations of a tumor image, to assess the progression (or regression) of tumors using statistical or machine learning models [1]}, [2]}. The main drawbacks of using voxel-intensity summary statistics/features are: (i) they do not capture the entire information in the voxel intensity histogram; (ii) there is subjectivity in the choice of the number of features and the location where they are computed. Consequently, the radiomic/radiogenomic analyses using summary features are unable to detect potential small-scale and sensitive changes in the tumor [3]}. To address these challenges, we propose a de novo strategy for quantifying the tumor heterogeneity using voxel-intensity based imaging phenotypes that mimic the tumor evolution process.
